Digital Marketing Strategies in the Era of Big Data: A Comparative Analysis of Consumer Engagement in Online Retail

Abstract

This research paper explores the transformative impact of big data on digital marketing strategies within the online retail sector, with a particular focus on Customer Lifetime Value (CLV) prediction and consumer engagement. The study examines how online retailers leverage big data and machine learning techniques to personalize marketing efforts, optimize customer segmentation, and enhance real-time decision-making. Through a combination of literature review and a detailed comparative analysis, the paper highlights the effectiveness of advanced analytics in driving higher conversion rates, improving customer retention, and fostering personalized shopping experiences. Key findings indicate that while larger retailers benefit from more sophisticated data infrastructure, smaller retailers can achieve significant gains through targeted, data-driven strategies. The paper also discusses the challenges related to data privacy and the technical complexities of managing large datasets. Deployment diagrams illustrate the integration of CLV prediction models and big data analytics into retail platforms, providing a visual framework for understanding these systems. Ultimately, the research underscores the critical role of big data in shaping the future of digital marketing, offering insights into best practices and strategic recommendations for online retailers aiming to enhance consumer engagement through data-driven approaches.